Towards real-time scheduling of virtual machines without kernel modifications

Åsberg, Mikael; Forsberg, Nils; Nolte, Thomas; Kato, Shigeo

doi:10.1109/etfa.2011.6059185

Cited by 20 publications

(10 citation statements)

References 23 publications

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“…VM migration practice exploits live or non-live patterns to move a complete virtual server across physical machines to successfully attain load balancing (Shiraz et al, 2013b, Yao et al, 2014, power efficiency (Dong et al, 2013;Mishra et al, 2012;Shrivastava et al, 2011), fault tolerance (Nagarajan et al, 2007, NGUYEN et al, 2013Thein and Park, 2009), and system maintenance (Asberg et al, 2011;Liu et al, 2011;Wu et al, 2013;Zhou et al, 2013) within a DC. A live VM migration pattern guarantees continuous service provisioning to the hosted applications during the VM memory transfer process (Kapil et al, 2013;Shribman and Hudzia, 2013), whereas non-live VM migration (Kozuch and Satyanarayanan, 2002;Wang et al, 2010a) suspends application execution prior to memory image transfer (Aikema et al, 2012;Glazer and Tropper, 1993;Satyanarayanan, 2002, Kozuch et al, 2002;Milojičić et al, 2000).…”

Section: Virtual Machine Migrationmentioning

confidence: 98%

“…Virtualization technology, the backbone of CloudCom, proactively offers scalable services to customers Virtualization employs a hypervisor to proficiently manage several VMs running on a single physical server and to efficiently utilize cloud resources (Barham et al, 2003, Bugnion et al, 2012Tao et al, 2012;Younge et al, 2011). However, co-hosting multiple VMs degrades application performance due to high resource contention (Asberg et al, 2011;Habib, 2008;Hu et al, 2013;Nathan et al, 2013;Younge et al, 2011). To improve application performance, the migration daemon migrates VM(s) to a resourcerich server in order to reduce the degree of resource contention (Jeong et al, 2013;Shuja et al, 2012;Mishra and Jaiswal, 2012;Moura Silva et al, 2007;Pop et al, 2012;Yao et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Ahmad

Gani

Hamid

et al. 2015

Journal of Network and Computer Applications

319

120

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Section: Virtual Machine Migrationmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Ahmad

Gani

Hamid

et al. 2015

Journal of Network and Computer Applications

319

120

View full text Add to dashboard Cite

“…//compute the error with respect to the next higher layer, k (15) for each weight w ij in network (16)…”

Section: Algorithmmentioning

confidence: 99%

“…The experimentation is carried in both WiFi and 3G. Cloud server is created using VirtualBox 4.0.32 [14,15], which used type-2 hypervisor. After carrying out the experimentation, we have defined QoS range for the services and defined the training table(SLA).…”

Section: Algorithmmentioning

confidence: 99%

Mobile Cloud Service Selection using Back Propagation Neural Network

Ahmed¹,

Sabyasachi²,

Choudhury³

2015

IJCA

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Cloud computing is a paradigm in high performance computing, focuses on provisioning ubiquitous computing with the help of Software and/or Hardware Virtualization. In Mobile Cloud Computing (MCC), mobile/portable devices access cloud resources through wireless communication(GPRS/3G/WiFi etc). MCC enhances the mobility of the cloud user which solves cloud computing issues such as Unreliability, Quality-of-Service (QoS), etc. Recently QoS has emerged as a one of the challenging issue in MCC which impact to the large number of mobile users and businesses. The QoS in MCC degrades mainly due to its limited bandwidth, network congestion, user mobility, etc. In this paper, we have proposed a mobile cloud computing framework that facilitates the mobile client to access cloud services with a high degree of QoS based on the network condition of the connection. We proposed a new QoS based mobile cloud computing framework . Back Propagation Neural Network (BPNN) is being used for predicting and selecting appropriate cloud service for the mobile client. We have implemented the proposed framework taking QoS parameters: Packet Delivery Ratio (PDR), Transmission Rate, and Delay in a mobile cloud computing environment. At the end, we have compared our model with the random selection approach and it shows that the proposed model gives better performance.

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“…VMs are transferred to another server to ensure normal service operations, and this is carried out by studying fault tolerance trigger points and the fault tolerance model. The preventive fault tolerance method also mainly involves workload balancing technology [18,19], energy saving technology [20], system consistence technology [21], and so on. Zhang et al [22] analyzed a running resource state with the hidden Markov model, calculated the future running state probability of a system, and conducted dynamic adjustments in system resource allocation to increase the efficiency of VM recovery against failures.…”

Section: State Of the Artmentioning

confidence: 99%

Study on Fault Tolerance Method in Cloud Platform based on Workload Consolidation Model of Virtual Machine

Li¹,

Liu²,

Tong³

2017

JESTR

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The fault tolerance method of virtual machines (VM) guarantees reliability to the service capability of cloud platforms. VM workloads are dynamic and uncertain, and thus, they affect the reliability and task processing capability of entire cloud platforms. In this study, a fault tolerance method based on the VM workload consolidation model was proposed to solve problems concerning the reliability and task processing capability of cloud platforms caused by VM workloads, thus improving the reliability of VMs and overall performance of cloud platforms. First, the method was analyzed on the basis of the distinct relationship of VM workload and VM reliability and task processing capability. Then, the workload state of VM was predicted and analyzed by linear regression using VM workload monitoring data, and the VM workload consolidation algorithm was constructed based on expected workload constraint and optimization of fault tolerance time. Finally, the fault tolerance method based on the VM workload consolidation model was compared with the Radom method and the Max method. Research results demonstrate the potential of the proposed method to improve VM reliability in cloud platforms by 20% and 47% compared with those for the Radom and Max methods, respectively. In the same workload phase, the task completion rate of the proposed method increased significantly (15% and 30%, and 22% and 30%), and the percentages were higher than those for the Radom and Max methods, respectively. Moreover, the proposed method shortened task response time. This study concludes that the workload consolidation of VMs can increase the reliability and task processing capability of VMs. This proposed method can provide technological support to the fault tolerance of VMs in cloud platforms.

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Towards real-time scheduling of virtual machines without kernel modifications

Cited by 20 publications

References 23 publications

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Mobile Cloud Service Selection using Back Propagation Neural Network

Study on Fault Tolerance Method in Cloud Platform based on Workload Consolidation Model of Virtual Machine

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